The input to the transformer module is the set of positive ROI features associated with the object category. Each feature \(f_i\)  in \(f\)  (comparable to the image patches in [1]}) is sent as a token to the linear projection layer, which gives a set of feature embeddings \(f^{\prime } \in \mathbb {R}^{N_{roi}\times D}\)  that are input to the encoder block. The multi-head self attention layer (MSA) [2]}, calls several self-attention operations in parallel in which a linear layer is used to encode and represent each token in \(f^{\prime }\)  as a value \(v\)  and a corresponding query \(q_i\)  and key \(k_j\)  pair. A weighted sum of all values in \(f^{\prime }\)  is computed for each token where the attention weight is
\(A_{ij} = \text{softmax}\left(\frac{qk^T}{\sqrt{d}}\right),\) 
